Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression.

Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progression over time is vital for clinical diagnosis and treatment. For this, numerous studies have implemented structural magnetic resonance imaging (MRI) to model AD progression, focusing on three integral aspects: 1) temporal variability; 2) incomplete observations; and 3) temporal geometric characteristics. However, many pioneer deep learning-based approaches addressing data variability and sparsity have yet to consider inherent geometrical properties sufficiently. These properties are integral to modeling as they correlate with brain region size, thickness, volume, and shape in AD progression. The ordinary differential equation-based geometric modeling method (ODE-RGRU) has recently emerged as a promising strategy for modeling time-series data by intertwining a recurrent neural network (RNN) and an ODE in Riemannian space. Despite its achievements, ODE-RGRU encounters limitations when extrapolating positive definite symmetric matrices from incomplete samples, leading to feature reverse occurrences that are particularly problematic, especially within the clinical facet. Therefore, this study proposes a novel geometric learning approach that models longitudinal MRI biomarkers and cognitive scores by combining three modules: topological space shift, ODE-RGRU, and trajectory estimation. We have also developed a training algorithm that integrates the manifold mapping with monotonicity constraints to reflect measurement transition irreversibility. We verify our proposed method's efficacy by predicting clinical labels and cognitive scores over time in regular and irregular settings. Furthermore, we thoroughly analyze our proposed framework through an ablation study.

Glycocalyx-Mimicking Nanoparticles with Differential Organ Selectivity for Drug Delivery and Therapy.

Organ-selective drug delivery is expected to maximize the efficacy of various therapeutic modalities while minimizing their systemic toxicity. Lipid nanoparticles and polymersomes can direct the organ-selective delivery of mRNAs or gene editing machineries, but their delivery is limited to mostly liver, spleen, and lung. A platform that enables delivery to these and other target organs is urgently needed. Here, a library of glycocalyx-mimicking nanoparticles (GlyNPs) comprising five randomly combined sugar moieties is generated, and direct in vivo library screening is used to identify GlyNPs with preferential biodistribution in liver, spleen, lung, kidneys, heart, and brain. Each organ-targeting GlyNP hit show cellular tropism within the organ. Liver, kidney, and spleen-targeting GlyNP hits equipped with therapeutics effectively can alleviate the symptoms of acetaminophen-induced liver injury, cisplatin-induced kidney injury, and immune thrombocytopenia in mice, respectively. Furthermore, the differential organ targeting of GlyNP hits is influenced not by the protein corona but by the sugar moieties displayed on their surface. It is envisioned that the GlyNP-based platform may enable the organ- and cell-targeted delivery of therapeutic cargoes.

Longitudinal Magnetic Resonance Imaging with ROS-Responsive Bilirubin Nanoparticles Enables Monitoring of Nonalcoholic Steatohepatitis Progression to Cirrhosis.

Despite the vital importance of monitoring the progression of nonalcoholic fatty liver disease (NAFLD) and its progressive form, nonalcoholic steatohepatitis (NASH), an efficient imaging modality that is readily available at hospitals is currently lacking. Here, a new magnetic-resonance-imaging (MRI)-based imaging modality is presented that allows for efficient and longitudinal monitoring of NAFLD and NASH progression. The imaging modality uses manganese-ion (Mn2+)-chelated bilirubin nanoparticles (Mn@BRNPs) as a reactive-oxygen-species (ROS)-responsive MRI imaging probe. Longitudinal T1-weighted MR imaging of NASH model mice is performed after injecting Mn@BRNPs intravenously. The MR signal enhancement in the liver relative to muscle gradually increases up to 8 weeks of NASH progression, but decreases significantly as NASH progresses to the cirrhosis-like stage at weeks 10 and 12. A new dual input pseudo-three-compartment model is developed to provide information on NASH stage with a single MRI scan. It is also demonstrated that the ROS-responsive Mn@BRNPs can be used to monitor the efficacy of potential anti-NASH drugs with conventional MRI. The findings suggest that the ROS-responsive Mn@BRNPs have the potential to serve as an efficient MRI contrast for monitoring NASH progression and its transition to the cirrhosis-like stage.

EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning.

Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, including the lack of adequate information while using linear low-order FC as inputs to the model, not considering individual characteristics (i.e., different symptoms or varying stages of severity) among patients with ASD, and the non-explainability of the decision process. To cover these limitations, we propose a novel explainability-guided region of interest (ROI) selection (EAG-RS) framework that identifies non-linear high-order functional associations among brain regions by leveraging an explainable artificial intelligence technique and selects class-discriminative regions for brain disease identification. The proposed framework includes three steps: (i) inter-regional relation learning to estimate non-linear relations through random seed-based network masking, (ii) explainable connection-wise relevance score estimation to explore high-order relations between functional connections, and (iii) non-linear high-order FC-based diagnosis-informative ROI selection and classifier learning to identify ASD. We validated the effectiveness of our proposed method by conducting experiments using the Autism Brain Imaging Database Exchange (ABIDE) dataset, demonstrating that the proposed method outperforms other comparative methods in terms of various evaluation metrics. Furthermore, we qualitatively analyzed the selected ROIs and identified ASD subtypes linked to previous neuroscientific studies.

Module of Axis-based Nexus Attention for weakly supervised object localization.

Weakly supervised object localization tasks remain challenging to identify and segment an entire object rather than only discriminative parts of the object. To tackle this problem, corruption-based approaches have been devised, which involve the training of non-discriminative regions by corrupting (e.g., erasing) the input images or intermediate feature maps. However, this approach requires an additional hyperparameter, the corrupting threshold, to determine the degree of corruption and can unfavorably disrupt training. It also tends to localize object regions coarsely. In this paper, we propose a novel approach, Module of Axis-based Nexus Attention (MoANA), which helps to adaptively activate less discriminative regions along with the class-discriminative regions without an additional hyperparameter, and elaborately localizes an entire object. Specifically, MoANA consists of three mechanisms (1) triple-view attentions representation, (2) attentions expansion, and (3) features calibration mechanism. Unlike other attention-based methods that train a coarse attention map with the same values across elements in feature maps, MoANA trains fine-grained values in an attention map by assigning different attention values to each element. We validated MoANA by comparing it with various methods. We also analyzed the effect of each component in MoANA and visualized attention maps to provide insights into the calibration.

Deep joint learning of pathological region localization and Alzheimer's disease diagnosis.

The identification of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) has been studied based on the subtle morphological changes in the brain. One of the typical approaches is a deep learning-based patch-level feature representation. For this approach, however, the predetermined patches before learning the diagnostic model can limit classification performance. To mitigate this problem, we propose the BrainBagNet with a position-based gate (PG), which applies position information of brain images represented through the 3D coordinates. Our proposed method represents the patch-level class evidence based on both MR scan and position information for image-level prediction. To validate the effectiveness of our proposed framework, we conducted comprehensive experiments comparing it with state-of-the-art methods, utilizing two publicly available datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle (AIBL) dataset. Furthermore, our experimental results demonstrate that our proposed method outperforms the existing competing methods in terms of classification performance for both AD diagnosis and mild cognitive impairment conversion prediction tasks. In addition, we performed various analyses of the results from diverse perspectives to obtain further insights into the underlying mechanisms and strengths of our proposed framework. Based on the results of our experiments, we demonstrate that our proposed framework has the potential to advance deep-learning-based patch-level feature representation studies for AD diagnosis and MCI conversion prediction. In addition, our method provides valuable insights, such as interpretability, and the ability to capture subtle changes, into the underlying pathological processes of AD and MCI, benefiting both researchers and clinicians.

Anti-inflammatory Glycocalyx-Mimicking Nanoparticles for Colitis Treatment: Construction and In Vivo Evaluation.

Common medications for treating inflammatory bowel disease (IBD) have limited therapeutic efficacy and severe adverse effects. This underscores the urgent need for novel therapeutic approaches that can effectively target inflamed sites in the gastrointestinal tract upon oral administration, exerting potent therapeutic efficacy while minimizing systemic effects. Here, we report the construction and in vivo therapeutic evaluation of a library of anti-inflammatory glycocalyx-mimicking nanoparticles (designated GlyNPs) in a mouse model of IBD. The anti-inflammatory GlyNP library was created by attaching bilirubin (BR) to a library of glycopolymers composed of random combinations of the five most naturally abundant sugars. Direct in vivo screening of 31 BR-attached anti-inflammatory GlyNPs via oral administration into mice with acute colitis led to identification of a candidate GlyNP capable of targeting macrophages in the inflamed colon and effectively alleviating colitis symptoms. These findings suggest that the BR-attached GlyNP library can be used as a platform to identify anti-inflammatory nanomedicines for various inflammatory diseases.

Bilirubin Nanomedicine Rescues Intestinal Barrier Destruction and Restores Mucosal Immunity in Colitis.

Inflammatory bowel disease (IBD) manifests as intestinal barrier destruction, mucosal immunity dysregulation, and disrupted gut microbiome homeostasis. Conventional anti-inflammatory medications for IBD therapy partially alleviate symptoms but are unable to restore normal barrier and immune function. Here, we report a nanomedicine comprising bilirubin (BR)-attached low-molecular-weight, water-soluble chitosan nanoparticles (LMWC-BRNPs), that promotes restoration of the intestinal barrier, mucosal immunity, and the gut microbiome, thereby exerting robust therapeutic efficacy. In a mouse model of dextran sulfate sodium salt (DSS)-induced colitis, orally administered LMWC-BRNPs were retained in the GI tract much longer than other nonmucoadhesive BRNPs owing to the mucoadhesiveness of LMWC via electrostatic interaction. Treatment with LMWC-BRNPs led to considerable recovery of the damaged intestinal barrier compared with the current IBD medication, 5-aminosalicylic acid (5-ASA). Orally administered LMWC-BRNPs were taken up by pro-inflammatory macrophages and inhibited their activity. They also concurrently increased the population of regulatory T cells, thereby leading to the recovery of dysregulated mucosal immunity. An analysis of the gut microbiome revealed that LMWC-BRNPs treatment significantly attenuated the increase Turicibacter, an inflammation-related microorganism, resulting in protection of gut microbiome homeostasis. Taken together, our findings indicate that LMWC-BRNPs restored normal functions of the intestine and have high potential for use as a nanomedicine for IBD therapy.

Classification of hepatobiliary scintigraphy patterns in segmented gallbladder according to anatomical discordance.

BACKGROUND: Hepatobiliary scintigraphy (HBS) is a useful diagnostic imaging technique that uses radiotracers to evaluate the function of the gallbladder (GB) and biliary system. In segmented GB, some HBS images reveal a discordant GB boundary as compared to anatomical images. AIM: To evaluate the characteristics of HBS in segmented GB and determine the clinical relevance according to HBS characteristics. METHODS: A total of 268 patients with chronic cholecystitis, gallstones, or biliary colic symptoms who underwent HBS between 2011 and 2020 were enrolled. Segmented GB was defined as segmental luminal narrowing of the GB body on computed tomography (CT) or magnetic resonance (MR) images, and HBS was examined 1 mo before or after CT or MR. Segmented GB was classified into 3 types based on the filling and emptying patterns of the proximal and distal segments according to the characteristics of HBS images, and GB ejection fraction (GBEF) was identified: Type 1 was defined as a normal filling and emptying pattern; Type 2 was defined as an emptying defect on the distal segment; and Type 3 was defined as a filling defect in the distal segment. RESULTS: Segmented GB accounted for 63 cases (23.5%), including 36 patients (57.1%) with Type 1, 18 patients (28.6%) with Type 2, and 9 patients (14.3%) with Type 3 emptying pattern. Thus, approximately 43% of HBS images showed a discordant pattern as compared to anatomical imaging of segmented GB. Although there were no significant differences in clinical symptoms, rate of cholecystectomy, or pathological findings based on the type, most gallstones occurred in the distal segment. Reported GBEF was 62.50% ± 24.79% for Type 1, 75.89% ± 17.21% for Type 2, and 88.56% ± 7.20% for Type 3. Type 1 showed no difference in reported GBEF compared to the non-segmented GB group (62.50% ± 24.79% vs 67.40% ± 21.78%). In contrast, the reported GBEF was higher in Types 2 and 3 with defective emptying and filling when compared to Type 1 (80.11% ± 15.70% vs 62.57% ± 24.79%; P = 0.001). CONCLUSION: In segmented GB, discordance in the filling patterns detected by HBS and anatomical imaging could lead to misinterpretation of GBEF. For this reason, clinicians should be cautious when interpreting HBS results in patients with segmented GB.

Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning.

Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Using clinically-guided prototype learning, we propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. Considering this pseudo map as an enriched reference, we employ an estimating network to approximate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from clinical and morphological perspectives. During the inference, this estimated likelihood map served as a substitute for unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.

Nanoparticles derived from naturally occurring metal chelators for theranostic applications.

Metals are indispensable for the activities of all living things, from single-celled organisms to higher organisms, including humans. Beyond their intrinsic quality as metal ions, metals help creatures to maintain requisite biological processes by forming coordination complexes with endogenous ligands that are broadly distributed in nature. These types of naturally occurring chelating reactions are found through the kingdoms of life, including bacteria, plants and animals. Mimicking these naturally occurring coordination complexes with intrinsic biocompatibility may offer an opportunity to develop nanomedicine toward clinical applications. Herein, we introduce representative examples of naturally occurring coordination complexes in a selection of model organisms and highlight such bio-inspired metal-chelating nanomaterials for theranostic applications.

Combination of a STAT3 inhibitor with anti-PD-1 immunotherapy is an effective treatment regimen for a vemurafenib-resistant melanoma.

Patients with BRAFV600E-mutant melanoma are effectively treated with the BRAF-inhibiting drug, vemurafenib, but soon develop drug resistance, limiting vemurafenib's therapeutic efficacy. Constitutive activation of STAT3 in cancer cells and immune cells in the tumor microenvironment (TME) is a crucial contributor to the development of drug resistance and immune evasion in most cancers. Here, we investigated the antitumor efficacy and TME remodeling by APTSTAT3-9R, a cell-permeable STAT3 inhibitory peptide, as a strategy to treat vemurafenib-resistant melanoma. We found that vemurafenib-resistant melanoma remodels into immunosuppressive TME by increasing the expression of specific chemokines to facilitate the infiltration of immunosuppressive immune cells, such as myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs). Intratumoral treatment of APTSTAT3-9R led to a reduction in the population of MDSCs and TAMs, while increasing infiltration of cytotoxic T lymphocytes in the TME. Moreover, combination therapy with APTSTAT3-9R and an anti-PD-1 antibody enhanced significant suppression of tumor growth by decreasing infiltration of these immunosuppressive immune cells while increasing the infiltration and cytotoxicity of CD8+ T cells. These findings suggest that combined blockade of STAT3 and PD-1 signaling pathways may be an effective treatment option for overcoming poor therapeutic outcomes associated with drug-resistant BRAF-mutant melanoma.

Systematic Screening and Therapeutic Evaluation of Glyconanoparticles with Differential Cancer Affinities for Targeted Cancer Therapy.

Cancer-targeting ligands used for nanomedicines have been limited mostly to antibodies, peptides, aptamers, and small molecules thus far. Here, a library of glycocalyx-mimicking nanoparticles as a platform to enable screening and identification of cancer-targeting nanomedicines is reported. Specifically, a library of 31 artificial glycopolymers composed of either homogeneous or heterogeneous display of five different sugar moieties (ß-glucose, ß-galactose, α-mannose, ß-N-acetyl glucosamine, and ß-N-acetyl galactosamine) is converted to a library of glyconanoparticles (GlyNPs). GlyNPs optimal for targeting CT26, DU145, A549, and PC3 tumors are systematically screened and identified. The cypate-conjugated GlyNP displaying α-mannose and ß-N-acetyl glucosamine show selective targeting and potent photothermal therapeutic efficacy against A549 human lung tumors. The docetaxel-contained GlyNP displaying ß-glucose, ß-galactose, and α-mannose demonstrate targeted chemotherapy against DU145 human prostate tumors. The results presented herein collectively demonstrate that the GlyNP library is a versatile platform enabling the identification of cancer-targeting glyconanoparticles and suggest its potential applicability for targeting various diseased cells beyond cancer.

A Deep Generative-Discriminative Learning for Multimodal Representation in Imaging Genetics.

Imaging genetics, one of the foremost emerging topics in the medical imaging field, analyzes the inherent relations between neuroimaging and genetic data. As deep learning has gained widespread acceptance in many applications, pioneering studies employed deep learning frameworks for imaging genetics. However, existing approaches suffer from some limitations. First, they often adopt a simple strategy for joint learning of phenotypic and genotypic features. Second, their findings have not been extended to biomedical applications, e.g., degenerative brain disease diagnosis and cognitive score prediction. Finally, existing studies perform insufficient and inappropriate analyses from the perspective of data science and neuroscience. In this work, we propose a novel deep learning framework to simultaneously tackle the aforementioned issues. Our proposed framework learns to effectively represent the neuroimaging and the genetic data jointly, and achieves state-of-the-art performance when used for Alzheimer's disease and mild cognitive impairment identification. Furthermore, unlike the existing methods, the framework enables learning the relation between imaging phenotypes and genotypes in a nonlinear way without any prior neuroscientific knowledge. To demonstrate the validity of our proposed framework, we conducted experiments on a publicly available dataset and analyzed the results from diverse perspectives. Based on our experimental results, we believe that the proposed framework has immense potential to provide new insights and perspectives in deep learning-based imaging genetics studies.

A bilirubin-derived nanomedicine attenuates the pathological cascade of pulmonary fibrosis.

Pulmonary fibrosis is an irreparable and life-threatening disease with only limited therapeutic options. The recent outbreak of COVID-19 has caused a sharp rise in the incidence of pulmonary fibrosis owing to SARS-CoV-2 infection-mediated acute respiratory distress syndrome (ARDS). The considerable oxidative damage caused by locally infiltrated immune cells plays a crucial role in ARDS, suggesting the potential use of antioxidative therapeutics. Here, we report the therapeutic potential of nanoparticles derived from the endogenous antioxidant and anti-inflammatory bile acid, bilirubin (BRNPs), in treating pulmonary fibrosis in a bleomycin-induced mouse model of the disease. Our results demonstrate that BRNPs can effectively reduce clinical signs in mice, as shown by histological, disease index evaluations, and detection of biomarkers. Our findings suggest that BRNPs, with their potent antioxidant and anti-inflammatory effects, long blood circulation half-life, and preferential accumulation at the inflamed site, are potentially a viable clinical option for preventing Covid-19 infection-associated pulmonary fibrosis.

Targeting the tumor microenvironment with amphiphilic near-infrared cyanine nanoparticles for potentiated photothermal immunotherapy.

Despite the potential of photothermal therapy (PTT) for cancer treatments, PTT alone has limitations in treating metastatic tumors and preventing tumor recurrence, highlighting the need to combine PTT with immunotherapy. This study reports tumor microenvironment (TME)-targeting, near-infrared (NIR) dye derivative-based nanomedicine for effective combined PTT-immunotherapy. Amphiphilic NIR dye cyanine derivatives are used not only for constructing the nanoparticle mass, but also for creating a stable complex with CpG adjuvant; a peptide specific to fibronectin extra domain B (APTEDB) is also introduced as a TME-targeting ligand, yielding the TME-targeting nanomedicine, APTEDB-cyNP@CpG. APTEDB-cyNP@CpG shows cancer-targeting ability in EDB-overexpressing CT26 colon tumor-bearing mice. When combined with laser irradiation, it induces immunogenic cell death (ICD) and subsequently leads to significant increase in CD8+ T cell population in the tumor, resulting in greater antitumor therapeutic efficacy than does cyNP@CpG lacking the TME-targeting ligand. Moreover, the combination of APTEDB-cyNP@CpG-based PTT and an immune checkpoint blockade (ICB) antibody leads to remarkable antitumor efficacy against the laser-irradiated primary tumor as well as distant tumor through potentiation of systemic cancer cell-specific T cell immunity. Furthermore, the PTT-immunotherapy combination regimen is highly effective in inhibiting tumor recurrence and metastasis.

Deep recurrent model for individualized prediction of Alzheimer's disease progression.

Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developing AD in its earliest time. While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling. Under the same problem settings, in this work, we propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status along with cognitive scores at multiple future time points. However, in handling time series data, it generally faces many unexpected missing observations. In regard to such an unfavorable situation, we define a secondary problem of estimating those missing values and tackle it in a systematic way by taking account of temporal and multivariate relations inherent in time series data. Concretely, we propose a deep recurrent network that jointly tackles the four problems of (i) missing value imputation, (ii) phenotypic measurements forecasting, (iii) trajectory estimation of a cognitive score, and (iv) clinical status prediction of a subject based on his/her longitudinal imaging biomarkers. Notably, the learnable parameters of all the modules in our predictive models are trained in an end-to-end manner by taking the morphological features and cognitive scores as input, with our circumspectly defined loss function. In our experiments over The Alzheimers Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge cohort, we measured performance for various metrics and compared our method to competing methods in the literature. Exhaustive analyses and ablation studies were also conducted to better confirm the effectiveness of our method.

Bilirubin nanomedicine ameliorates the progression of experimental autoimmune encephalomyelitis by modulating dendritic cells.

Although the cause of multiple sclerosis (MS) is unclear, an autoimmune attack on myelin-based coating layers of nerve cells in the brain and spinal cord is the main feature of the disease, highlighting modulation of the immune response to myelin as a feasible therapeutic approach. Here, we report the potential of bilirubin nanoparticles (BRNPs) based on the endogenous antioxidant and anti-inflammatory agent, bilirubin, as a therapeutic nanomedicine for MS. In a mouse model of experimental autoimmune encephalomyelitis (EAE), multiple intravenous injections of BRNPs significantly delayed disease onset and suppressed disease progression and severity as well as disease incidence rate without systemic immunosuppression. Following intravenous injection, BRNPs accumulated more extensively and were retained longer in secondary lymphoid organs of EAE-induced mice compared with non-immunized control mice, including in inguinal lymph nodes (iLNs) and spleens, where antigen presenting cells (APCs) activated by the myelin antigen are abundant. Studies of the underlying mechanism of action further revealed that BRNPs negatively regulated the differentiation of naïve CD4+ T cells into T helper 17 (Th17) cells by inhibiting maturation of APCs through scavenging of reactive oxygen species (ROS) overproduced in both dendritic cells (DCs) and macrophages upon antigen uptake. These findings indicate that BRNPs have the potential to be used as a new therapeutic nanomedicine for treatment of various CD4+ T cell-associated autoimmune diseases.

Glutathione-Responsive Gold Nanoparticles as Computed Tomography Contrast Agents for Hepatic Diseases.

Glutathione (GSH) is produced at high levels in the normal liver, but its production is considerably reduced under certain pathological conditions. Accordingly, an imaging probe capable of visualizing the altered GSH level in the liver would be a useful tool for monitoring hepatic functions or diseases. Here, we report a gold nanoparticle (AuNP)-based computed tomography (CT) contrast agent that undergoes a change in colloidal stability in response to GSH levels, resulting in differential CT signal intensity between normal (higher intensity) and pathological (lower intensity) livers, enabling imaging of hepatic function. This GSH-responsive CT contrast agent, prepared by coating AuNPs with PEGylated bilirubin (PEG-BR), shows serum stability and high sensitivity to GSH. The resulting poly(ethylene glycol) (PEG)-BR@AuNPs preferentially accumulate in the normal liver, as evidenced by strongly enhanced CT intensity, but fail to do so in a GSH-depleted mouse model, where the CT signal in the liver was substantially decreased. In addition, injection of PEG-BR@AuNPs caused a greater reduction in CT signals in the liver in a drug-induced acute liver failure model than in healthy normal mice. These findings suggest that GSH-responsive PEG-BR@AuNPs have the potential to be used as a CT contrast agent to detect various hepatic function-related diseases and liver-metastasized tumors.

Bilirubin nanomedicine alleviates psoriatic skin inflammation by reducing oxidative stress and suppressing pathogenic signaling.

Psoriasis is a prevalent chronic inflammatory skin disease characterized by thickening of the epidermis accompanied by lesional erythema, scaling, and induration as a result of abnormal proliferation of keratinocytes. During the development of psoriasis, levels of intracellular reactive oxygen species (ROS) within psoriatic lesions are elevated, activating a pro-inflammatory signaling cascade. Here, we evaluated the therapeutic efficacy and mode of action of bilirubin nanoparticles (BRNPs), based on the potent, endogenous antioxidant bilirubin, in a preclinical psoriasis model. We found that topical treatment of psoriatic lesions with BRNPs effectively attenuated upregulation of intracellular ROS levels within keratinocytes and ameliorated the symptoms of psoriasis. A subsequent mechanistic study showed that preventing oxidative stress in activated keratinocytes suppressed the secretion of inflammatory mediators and recruitment of immune cells. Subsequent expression of the antigen-presenting cell (APC) maturation markers, class II major histocompatibility complex (MHC class II), cluster of differentiation (CD) 80 and CD86, was significantly decreased, resulting in a reduction in the differentiation of naïve CD4+ T cells into interleukin (IL)-17-producing T-helper (Th) 17 cells. Unlike the commercial corticosteroid drug, clobetasol propionate (CLQ), BRNPs, composed of the endogenous antioxidant bilirubin and the approved polymer polyethylene glycol (PEG), did not exert systemic cytotoxicity. Collectively, these findings highlight the potential of BRNPs as a novel nanomedicine for ameliorating psoriasis-like skin inflammation through topical treatment and suggest that their use could be further expanded to treat other chronic skin inflammation diseases, including atopic dermatitis.